Reconstructing transcriptional regulatory networks is an important task in functional genomics. Data obtained from experiments that perturb genes by knockouts or RNA interference contain useful information for addressing the reconstruction problem. However, such data can be limited in size and/or expensive to acquire. On the other hand, observational data of the organism in steady state are more readily available, but their informational content is inadequate for the task at hand. I will discuss a new computational framework, involving a three-step algorithm, for estimating the underlying directed (cyclic) network using both perturbation screens and steady state gene expression data. In the first step, an exhaustive search method is combined with a fast heuristic, which couples a Monte Carlo technique with a fast search algorithm, to determine causal orderings of the genes which are consistent with the perturbation data. In the second step, a regulatory network is estimated !
for each ordering using a penalized likelihood based method, while in the third step a consensus network is constructed from the highest scored ones. Extensive numerical experiments show that the algorithm performs well in uncovering the underlying network and clearly outperforms competing approaches that rely only on a single data source. Further, it is established that the algorithm produces a consistent estimate of the regulatory network.